Feature-Specific Salience Maps in Human Cortex.

Priority map theory is a leading framework for understanding how various aspects of stimulus displays and task demands guide visual attention. Per this theory, the visual system computes a priority map, which is a representation of visual space indexing the relative importance, or priority, of locations in the environment. Priority is computed based on both salience, defined based on image-computable properties; and relevance, defined by an individual's current goals, and is used to direct attention to the highest-priority locations for further processing. Computational theories suggest that priority maps identify salient locations based on individual feature dimensions (e.g., color, motion), which are integrated into an aggregate priority map. While widely accepted, a core assumption of this framework, the existence of independent feature dimension maps in visual cortex, remains untested. Here, we tested the hypothesis that retinotopic regions selective for specific feature dimensions (color or motion) in human cortex act as neural feature dimension maps, indexing salient locations based on their preferred feature. We used fMRI activation patterns to reconstruct spatial maps while male and female human participants viewed stimuli with salient regions defined by relative color or motion direction. Activation in reconstructed spatial maps was localized to the salient stimulus position in the display. Moreover, the strength of the stimulus representation was strongest in the ROI selective for the salience-defining feature. Together, these results suggest that feature-selective extrastriate visual regions highlight salient locations based on local feature contrast within their preferred feature dimensions, supporting their role as neural feature dimension maps.SIGNIFICANCE STATEMENT Identifying salient information is important for navigating the world. For example, it is critical to detect a quickly approaching car when crossing the street. Leading models of computer vision and visual search rely on compartmentalized salience computations based on individual features; however, there has been no direct empirical demonstration identifying neural regions as responsible for performing these dissociable operations. Here, we provide evidence of a critical double dissociation that neural activation patterns from color-selective regions prioritize the location of color-defined salience while minimally representing motion-defined salience, whereas motion-selective regions show the complementary result. These findings reveal that specialized cortical regions act as neural "feature dimension maps" that are used to index salient locations based on specific features to guide attention.

Learned feature regularities enable suppression of spatially overlapping stimuli.

Contemporary theories of attentional control state that information can be prioritized based on selection history. Even though theories agree that selection history can impact representations of spatial location, which in turn helps guide attention, there remains disagreement on whether nonspatial features (e.g., color) are modulated in a similar way. While previous work has demonstrated color suppression using visual search tasks, it is possible that the location corresponding to the distractor was suppressed, consistent with a spatial mechanism of suppression. Here, we sought to rule out this possibility by testing whether similar suppression of a learned distractor color can occur for spatially overlapping visual stimuli. On a given trial, two spatially superimposed stimuli (line arrays) were tilted either left or right of vertical and presented in one of four distinct colors. Subjects performed a speeded report of the orientation of the "target" array with the most lines. Critically, the distractor array was regularly one color, and this high-probability color was never the color of the target array, which encouraged learned suppression. In two experiments, responses to the target array were fastest when the distractor array was in the high-probability color, suggesting participants suppressed the distractor color. Additionally, when regularities were removed, the high-probability distractor color continued to benefit speeded target identification for individual subjects (E1) but slowed target identification (E2) when presented in the target array. Together, these results indicate that learned suppression of feature-based regularities modulates target detection performance independent of spatial location and persists over time.

Guidance of attention from visual working memory is feature-based, not object-based: Implications for models of feature binding.

A classic question in visual working memory (VWM) research is whether features from the same object are bound directly in an integrated representation or are maintained separately and bound only indirectly though shared location. Here, we examined this question using a novel method that probed the effects of VWM on the guidance of attention (rather than requiring explicit access to VWM content, as has typically been used). Participants remembered two color-shape conjunction objects. During a retention-interval search task, they searched for a target letter among distractor letters superimposed over color-shape conjunction items. There were two critical conditions. In the same-object-match condition, one search item matched both the color and shape of a single remembered object. In the different-object-match condition, one search item matched the color from one remembered object and the shape from the other. Robust effects of VWM-based guidance were observed, both when probing the incidental guidance of attention (Experiments 1 and 2) and the strategic guidance of attention (Experiment 3). Critically, in none of the experiments was the magnitude of guidance greater for same-object-match than for different-object-match. The results indicate that the representational units of guidance from VWM are individual features rather than integrated objects. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The architecture of working memory: Features from multiple remembered objects produce parallel, coactive guidance of attention in visual search.

Theories of working memory (WM) differ in their claims about the number of items that can be maintained in a state that directly interacts with other, ongoing cognitive operations (termed the focus of attention). A similar debate has arisen in the literature on visual working memory (VWM), focused on the number of items that can simultaneously interact with attentional priority. In 3 experiments, we used a redundancy-gain paradigm to provide a comprehensive test of the latter question. Participants searched for 2 cued features (e.g., a color and a shape) within a search array. The cued feature values changed on a trial-by-trial basis, requiring VWM. The target (when present) could match 1 of the cued features (single-target trials) or both cued features (redundant-target trials). We tested whether response time distributions contained a substantial proportion of trials with redundant-target responses that were faster than predicted by 2 independent guidance processes operating in parallel (i.e., violations of the race-model inequality). Violations are consistent with a coactive architecture in which both cued values guide attention in parallel and sum on the priority map. Robust violations were observed in all cases predicted by the hypothesis that multiple items in VWM can guide attention simultaneously, and these results were inconsistent with the hypothesis that guidance is limited to a single item simultaneously. When considered in the larger context of the literature on VWM and attention, the present results are consistent with a model of WM architecture in which the focus of attention can maintain multiple, independent representations. (PsycInfo Database Record (c) 2020 APA, all rights reserved).